鑽石切工締造璀璨光芒
高折射率造就了鑽石的璀璨光芒,使其聞名於世。許多人會把鑽石切工等同於鑽石的形狀(圓形、心形、橢圓形、馬眼形、梨形)。實際上鑽石切工等級取決於其刻面與光線的相互作用結果。
鑽石淨度是指其無內含物和表面特徵的程度
天然鑽石是碳元素在高溫高壓的環境下形成的。而這一過程,也導致了每顆鑽石有內部的特徵,稱為“內含物”,在鑽石表面的特徵則稱為“表面特徵”。
對鑽石淨度的評定,包括了對上述特徵的數量、大小、可見度、類型、位置和對鑽石整體外觀的影響程度的鑑定。儘管世上沒有絕對完美無瑕的天然鑽石,但淨度越高的鑽石,價值越高。
GIA 的鑽石淨度標準分為 6 個類別,11 個等級。
鑽石克拉重量用於衡量鑽石的外觀尺寸
克拉是用於衡量鑽石重量的單位。1 克拉等於 200 毫克。
在對鑽石重量進行測量時,1 克拉又等同於 100 “分”,從而使克拉重量精確到小數點後兩位。珠寶商可以直接用“分” 來描述一顆重量小於1 克拉的鑽石。比如,0.25 克拉的鑽石又被稱作“二十五分”。但對於1 克拉以上的鑽石, 只能用精確到百分位的“克拉”來描述。比如,1.08 克拉的鑽石只能用“一點零八克拉” 來說明其重量。
在所有其他條件都一樣的情況下,鑽石越大,其稀有程度就越高,也越受青睞。因此,鑽石的價格會隨著鑽石克拉重量的增加而上升。
鑽石顏色實際上是指其無色的程度
對大多數寶石級鑽石來說,其顏色鑑定標準取決於它的無色程度。一顆不含化學雜質且結構完美的鑽石就像純淨的水滴一樣,是沒有色彩的,且具有更高的價值。GIA 創立了D 到Z 鑽石顏色分級系統,通過在精確受控的照明、觀測條件下,將鑽石與比色石比對,來鑑定其無色程度。
names(diamonds)
[1] "carat" "cut" "color" "clarity" "depth" "table"
[7] "price" "x" "y" "z" "price_grp"
vars= names(diamonds)
codebook= summary(diamonds) %>% split(col(.))
names(codebook)= vars
descript=
c('克拉','裁切等級','鑽石顏色','鑽石淨度', '總深度百分比','鑽石的最大刻面',
'價錢(美元)','長度(mm)', '寬度(mm)','深度(mm)')
cb = rbind.data.frame(descript, codebook) %>% t
kable(cb) %>% kable_classic(html_font = 'Times New Roman')
| carat | 克拉 | Min. :0.2000 | 1st Qu.:0.4000 | Median :0.7000 | Mean :0.7979 | 3rd Qu.:1.0400 | Max. :5.0100 | |
| cut | 裁切等級 | Fair : 1610 | Good : 4906 | Very Good:12082 | Premium :13791 | Ideal :21551 | ||
| color | 鑽石顏色 | D: 6775 | E: 9797 | F: 9542 | G:11292 | H: 8304 | I: 5422 | J: 2808 |
| clarity | 鑽石淨度 | SI1 :13065 | VS2 :12258 | SI2 : 9194 | VS1 : 8171 | VVS2 : 5066 | VVS1 : 3655 | (Other): 2531 |
| depth | 總深度百分比 | Min. :43.00 | 1st Qu.:61.00 | Median :61.80 | Mean :61.75 | 3rd Qu.:62.50 | Max. :79.00 | |
| table | 鑽石的最大刻面 | Min. :43.00 | 1st Qu.:56.00 | Median :57.00 | Mean :57.46 | 3rd Qu.:59.00 | Max. :95.00 | |
| price | 價錢(美元) | Min. : 326 | 1st Qu.: 950 | Median : 2401 | Mean : 3933 | 3rd Qu.: 5324 | Max. :18823 | |
| x | 長度(mm) | Min. : 0.000 | 1st Qu.: 4.710 | Median : 5.700 | Mean : 5.731 | 3rd Qu.: 6.540 | Max. :10.740 | |
| y | 寬度(mm) | Min. : 0.000 | 1st Qu.: 4.720 | Median : 5.710 | Mean : 5.735 | 3rd Qu.: 6.540 | Max. :58.900 | |
| z | 深度(mm) | Min. : 0.000 | 1st Qu.: 2.910 | Median : 3.530 | Mean : 3.539 | 3rd Qu.: 4.040 | Max. :31.800 | |
| price_grp | 克拉 | min-Q1 :13490 | Q1-median:13495 | median-Q3:13470 | Q3-Max :13485 |
diamonds %>%
ggplot(aes(x=cut,y=price, fill=cut)) +
geom_violin()->p; plotly::ggplotly(p,width = 9)
diamonds %>%
ggplot(aes(x=clarity,y=price, fill=clarity)) +
geom_violin()->p; plotly::ggplotly(p,width = 9)
diamonds$carat_grp= cut(diamonds$carat, fivenum(diamonds$carat),include.lowest = T)
Found more than one class "tbl_df" in cache; using the first, from namespace 'tibble'
Also defined by ‘memisc’
diamonds %>%
ggplot(aes(x=carat_grp,y=price, fill=carat_grp)) +
geom_violin()->p; plotly::ggplotly(p,width = 9)
diamonds %>%
ggplot(aes(x=color,y=price, fill=color)) +
geom_violin()->p; plotly::ggplotly(p,width = 9)
M=
diamonds %>%
dplyr::select(price, carat,depth,table,x,y,z) %>%
cor
M[lower.tri(M)] <- NA
M %>% pheatmap(display_numbers = T, number_color = 'white', cluster_rows=F, cluster_cols=F, na_col="white", color = rev(brewer.pal(10, 'RdBu')))
diamonds %>%
ggplot(aes(carat, price, col= color))+
geom_point() + facet_grid(cut~clarity)
diamonds$price_grp=
cut(diamonds$price, fivenum(diamonds$price), include.lowest = T, labels = c('<Q1','Q1-median','median-Q3', '>Q3'))
Found more than one class "tbl_df" in cache; using the first, from namespace 'tibble'
Also defined by ‘memisc’
library(moonBook)
mytable(price_grp~.,diamonds) %>% mytable2df() %>% kable() %>% kable_classic()
| name | <Q1 | Q1-median | median-Q3 | >Q3 | p |
|---|---|---|---|---|---|
| (N=13490) | (N=13495) | (N=13470) | (N=13485) | ||
| carat | 0.3 ± 0.0 | 0.5 ± 0.1 | 0.9 ± 0.2 | 1.4 ± 0.4 | 0.000 |
| cut | 0.000 | ||||
| - Fair | 88 ( 0.7%) | 462 ( 3.4%) | 667 ( 5.0%) | 393 ( 2.9%) | |
| - Good | 1057 ( 7.8%) | 1087 ( 8.1%) | 1634 (12.1%) | 1128 ( 8.4%) | |
| - Very Good | 3129 (23.2%) | 2533 (18.8%) | 3369 (25.0%) | 3051 (22.6%) | |
| - Premium | 2907 (21.5%) | 3069 (22.7%) | 3504 (26.0%) | 4311 (32.0%) | |
| - Ideal | 6309 (46.8%) | 6344 (47.0%) | 4296 (31.9%) | 4602 (34.1%) | |
| color | 0.000 | ||||
| - D | 1878 (13.9%) | 2047 (15.2%) | 1698 (12.6%) | 1152 ( 8.5%) | |
| - E | 2790 (20.7%) | 2984 (22.1%) | 2471 (18.3%) | 1552 (11.5%) | |
| - F | 2268 (16.8%) | 2642 (19.6%) | 2520 (18.7%) | 2112 (15.7%) | |
| - G | 2917 (21.6%) | 2962 (21.9%) | 2227 (16.5%) | 3186 (23.6%) | |
| - H | 2023 (15.0%) | 1448 (10.7%) | 2308 (17.1%) | 2525 (18.7%) | |
| - I | 1184 ( 8.8%) | 888 ( 6.6%) | 1459 (10.8%) | 1891 (14.0%) | |
| - J | 430 ( 3.2%) | 524 ( 3.9%) | 787 ( 5.8%) | 1067 ( 7.9%) | |
| clarity | 0.000 | ||||
| - I1 | 55 ( 0.4%) | 189 ( 1.4%) | 319 ( 2.4%) | 178 ( 1.3%) | |
| - SI2 | 1024 ( 7.6%) | 1530 (11.3%) | 4104 (30.5%) | 2536 (18.8%) | |
| - SI1 | 2933 (21.7%) | 2859 (21.2%) | 4119 (30.6%) | 3154 (23.4%) | |
| - VS2 | 3389 (25.1%) | 3109 (23.0%) | 2200 (16.3%) | 3560 (26.4%) | |
| - VS1 | 2297 (17.0%) | 2189 (16.2%) | 1433 (10.6%) | 2252 (16.7%) | |
| - VVS2 | 1784 (13.2%) | 1530 (11.3%) | 666 ( 4.9%) | 1086 ( 8.1%) | |
| - VVS1 | 1392 (10.3%) | 1356 (10.0%) | 458 ( 3.4%) | 449 ( 3.3%) | |
| - IF | 616 ( 4.6%) | 733 ( 5.4%) | 171 ( 1.3%) | 270 ( 2.0%) | |
| depth | 61.8 ± 1.2 | 61.7 ± 1.4 | 61.8 ± 1.7 | 61.7 ± 1.5 | 0.000 |
| table | 57.0 ± 2.1 | 57.2 ± 2.3 | 57.9 ± 2.3 | 57.8 ± 2.1 | 0.000 |
| price | 687.9 ± 149.4 | 1563.3 ± 443.6 | 3790.7 ± 852.7 | 9692.1 ± 3657.3 | 0.000 |
| x | 4.4 ± 0.2 | 5.2 ± 0.4 | 6.2 ± 0.4 | 7.2 ± 0.6 | 0.000 |
| z | 2.7 ± 0.1 | 3.2 ± 0.4 | 3.8 ± 0.3 | 4.4 ± 0.4 | 0.000 |
| carat_grp | 0.000 | ||||
| - [0.2,0.4] | 12076 (89.5%) | 2315 (17.2%) | 0 ( 0.0%) | 0 ( 0.0%) | |
| - (0.4,0.7] | 1413 (10.5%) | 9776 (72.4%) | 1573 (11.7%) | 9 ( 0.1%) | |
| - (0.7,1.04] | 1 ( 0.0%) | 1386 (10.3%) | 9434 (70.0%) | 2578 (19.1%) | |
| - (1.04,5.01] | 0 ( 0.0%) | 18 ( 0.1%) | 2463 (18.3%) | 10898 (80.8%) |
diamonds1 = diamonds
diamonds1$cut <- factor(diamonds1$cut, ordered=F)
diamonds1$color <- factor(diamonds1$color, ordered=F)
diamonds1$clarity <- factor(diamonds1$clarity, ordered=F)
m1 <- lm(I(log(price)) ~ I(carat^(1/3)), data = diamonds1)
m2 <- update(m1, ~ . + carat)
m3 <- update(m2, ~ . + cut)
m4 <- update(m3, ~ . + color)
m5 <- update(m4, ~ . + clarity)
mt = memisc::mtable(m1, m2, m3, m4, m5, sdigits=4, getSummary=NULL,
summary.stats=c("R-squared", "adj. R-squared", "sigma", "F","p",
"Log-likelihood", "Deviance", "AIC", "BIC", "N"))
| m1 | m2 | m3 | m4 | m5 | |||||||||||
| (Intercept) | 2 | . | 821*** | 1 | . | 039*** | 0 | . | 685*** | 0 | . | 891*** | −0 | . | 172*** |
| (0 | . | 006) | (0 | . | 019) | (0 | . | 020) | (0 | . | 018) | (0 | . | 011) | |
| I(carat^(1/3)) | 5 | . | 558*** | 8 | . | 568*** | 8 | . | 703*** | 8 | . | 438*** | 9 | . | 144*** |
| (0 | . | 007) | (0 | . | 032) | (0 | . | 031) | (0 | . | 028) | (0 | . | 016) | |
| carat | −1 | . | 137*** | −1 | . | 163*** | −0 | . | 992*** | −1 | . | 093*** | |||
| (0 | . | 012) | (0 | . | 011) | (0 | . | 010) | (0 | . | 006) | ||||
| cut: Good/Fair | 0 | . | 159*** | 0 | . | 158*** | 0 | . | 077*** | ||||||
| (0 | . | 007) | (0 | . | 006) | (0 | . | 004) | |||||||
| cut: Very Good/Fair | 0 | . | 236*** | 0 | . | 235*** | 0 | . | 112*** | ||||||
| (0 | . | 007) | (0 | . | 006) | (0 | . | 003) | |||||||
| cut: Premium/Fair | 0 | . | 236*** | 0 | . | 235*** | 0 | . | 135*** | ||||||
| (0 | . | 007) | (0 | . | 006) | (0 | . | 003) | |||||||
| cut: Ideal/Fair | 0 | . | 316*** | 0 | . | 316*** | 0 | . | 160*** | ||||||
| (0 | . | 007) | (0 | . | 006) | (0 | . | 003) | |||||||
| color: E/D | −0 | . | 026*** | −0 | . | 055*** | |||||||||
| (0 | . | 004) | (0 | . | 002) | ||||||||||
| color: F/D | −0 | . | 036*** | −0 | . | 096*** | |||||||||
| (0 | . | 004) | (0 | . | 002) | ||||||||||
| color: G/D | −0 | . | 063*** | −0 | . | 163*** | |||||||||
| (0 | . | 003) | (0 | . | 002) | ||||||||||
| color: H/D | −0 | . | 196*** | −0 | . | 256*** | |||||||||
| (0 | . | 004) | (0 | . | 002) | ||||||||||
| color: I/D | −0 | . | 295*** | −0 | . | 375*** | |||||||||
| (0 | . | 004) | (0 | . | 002) | ||||||||||
| color: J/D | −0 | . | 426*** | −0 | . | 511*** | |||||||||
| (0 | . | 005) | (0 | . | 003) | ||||||||||
| clarity: SI2 | 0 | . | 420*** | ||||||||||||
| (0 | . | 005) | |||||||||||||
| clarity: SI1 | 0 | . | 585*** | ||||||||||||
| (0 | . | 005) | |||||||||||||
| clarity: VS2 | 0 | . | 733*** | ||||||||||||
| (0 | . | 005) | |||||||||||||
| clarity: VS1 | 0 | . | 803*** | ||||||||||||
| (0 | . | 005) | |||||||||||||
| clarity: VVS2 | 0 | . | 934*** | ||||||||||||
| (0 | . | 005) | |||||||||||||
| clarity: VVS1 | 1 | . | 006*** | ||||||||||||
| (0 | . | 005) | |||||||||||||
| clarity: IF | 1 | . | 101*** | ||||||||||||
| (0 | . | 006) | |||||||||||||
| R-squared | 0 | . | 9236 | 0 | . | 9349 | 0 | . | 9391 | 0 | . | 9514 | 0 | . | 9839 |
| adj. R-squared | 0 | . | 9236 | 0 | . | 9349 | 0 | . | 9391 | 0 | . | 9514 | 0 | . | 9839 |
| sigma | 0 | . | 2805 | 0 | . | 2588 | 0 | . | 2504 | 0 | . | 2237 | 0 | . | 1286 |
| F | 652012 | . | 0628 | 387489 | . | 3661 | 138654 | . | 5235 | 87959 | . | 4667 | 173791 | . | 0840 |
| p | 0 | . | 0000 | 0 | . | 0000 | 0 | . | 0000 | 0 | . | 0000 | 0 | . | 0000 |
| Log-likelihood | −7962 | . | 4993 | −3631 | . | 3185 | −1837 | . | 4158 | 4235 | . | 2405 | 34091 | . | 2720 |
| Deviance | 4242 | . | 8307 | 3613 | . | 3602 | 3380 | . | 8373 | 2699 | . | 2124 | 892 | . | 2143 |
| AIC | 15930 | . | 9985 | 7270 | . | 6370 | 3690 | . | 8316 | −8442 | . | 4809 | −68140 | . | 5440 |
| BIC | 15957 | . | 6854 | 7306 | . | 2195 | 3761 | . | 9966 | −8317 | . | 9421 | −67953 | . | 7358 |
| N | 53940 | 53940 | 53940 | 53940 | 53940 | ||||||||||
|
Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05 |
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